{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/jermwatt/machine_learning_refined/blob/main/notes/5_Linear_regression/5_5_Weighted.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y06Ayti7-Ma4"
   },
   "source": [
    "## Chapter 5: Linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook contains interactive content from an early draft of the university textbook <a href=\"https://github.com/neonwatty/machine-learning-refined/tree/main\">\n",
    "Machine Learning Refined (2nd edition) </a>.\n",
    "\n",
    "The final draft significantly expands on this content and is available for <a href=\"https://github.com/neonwatty/machine-learning-refined/tree/main/chapter_pdfs\"> download as a PDF here</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YDp5E0fA-Ma5"
   },
   "source": [
    "# 5.5 Weighted Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K8FmiFxG-Ma5"
   },
   "source": [
    "Because regression cost functions are *summable over individual points* we can - as we will see in this Section - weight individual points in order to emphasize or de-emphasize their importance to a regression model.  This is called *weighted regression*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9AK3R2i5-Ma6",
    "outputId": "6be333ff-a0f1-4a5d-b5da-97977889dc38"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: github-clone in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (1.2.0)\n",
      "Requirement already satisfied: requests>=2.20.0 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from github-clone) (2.32.3)\n",
      "Requirement already satisfied: docopt>=0.6.2 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from github-clone) (0.6.2)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from requests>=2.20.0->github-clone) (3.4.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from requests>=2.20.0->github-clone) (3.10)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from requests>=2.20.0->github-clone) (2.2.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from requests>=2.20.0->github-clone) (2024.12.14)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "chapter_5_datasets already cloned!\n",
      "chapter_5_library already cloned!\n",
      "chapter_5_videos already cloned!\n"
     ]
    }
   ],
   "source": [
    "# install github clone - allows for easy cloning of subdirectories\n",
    "!pip install github-clone\n",
    "from pathlib import Path \n",
    "\n",
    "# clone datasets\n",
    "if not Path('chapter_5_datasets').is_dir():\n",
    "    !ghclone https://github.com/neonwatty/machine-learning-refined-notes-assets/tree/main/notes/5_Linear_regression/chapter_5_datasets\n",
    "else:\n",
    "    print('chapter_5_datasets already cloned!')\n",
    "\n",
    "# clone library subdirectory\n",
    "if not Path('chapter_5_library').is_dir():\n",
    "    !ghclone https://github.com/neonwatty/machine-learning-refined-notes-assets/tree/main/notes/5_Linear_regression/chapter_5_library\n",
    "else:\n",
    "    print('chapter_5_library already cloned!')\n",
    "\n",
    "# clone videos\n",
    "if not Path('chapter_5_videos').is_dir():\n",
    "    !ghclone https://github.com/neonwatty/machine-learning-refined-notes-assets/tree/main/notes/5_Linear_regression/chapter_5_videos\n",
    "else:\n",
    "    print('chapter_5_videos already cloned!')\n",
    "\n",
    "# append path for local library, data, and image import\n",
    "import sys\n",
    "sys.path.append('./chapter_5_library')\n",
    "sys.path.append('./chapter_5_datasets') \n",
    "sys.path.append('./chapter_5_videos') \n",
    "\n",
    "# import section helper\n",
    "import section_5_5_helpers\n",
    "\n",
    "# dataset paths\n",
    "data_path_1 = 'chapter_5_datasets/galileo_ramp_data.csv'\n",
    "data_path_2 = 'chapter_5_datasets/weighting_regression_data.csv'\n",
    "\n",
    "# video paths\n",
    "video_path_1 = 'chapter_5_videos/animation_4.mp4'\n",
    "\n",
    "# standard imports\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import Image, HTML\n",
    "from base64 import b64encode\n",
    "\n",
    "def show_video(video_path, width = 1000):\n",
    "    video_file = open(video_path, \"r+b\").read()\n",
    "    video_url = f\"data:video/mp4;base64,{b64encode(video_file).decode()}\"\n",
    "    return HTML(f\"\"\"<video width={width} controls><source src=\"{video_url}\"></video>\"\"\")\n",
    "\n",
    "# import autograd-wrapped numpy\n",
    "import autograd.numpy as np\n",
    "\n",
    "# this is needed to compensate for matplotlib notebook's tendancy to blow up images when plotted inline\n",
    "%matplotlib inline\n",
    "from matplotlib import rcParams\n",
    "rcParams['figure.autolayout'] = True\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DoUsdm-u-Ma6"
   },
   "source": [
    "##  Dealing with duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HZQ9ty26-Ma7"
   },
   "source": [
    "Imagine we have a linear regression dataset $\\left(x_1,y_1\\right),\\left(x_2,y_2\\right),...,\\left(x_P,y_P\\right)$ that contains multiple copies of the same point.  This can occur in a variety of contexts including\n",
    "\n",
    "    \n",
    "- experimental data (e.g., in physics, medicine, etc.,): if a repeated experiment produces  the same result\n",
    "    \n",
    "    \n",
    "- metadata-type datasets (e.g., census, customer databases): due to necessary / helpful pre-processing that quantizes (bins) input features in order to make human-in-the-loop analysis of the data / modeling easier\n",
    "    \n",
    "    \n",
    "In such instances 'duplicate' datapoints should not be thrown away, since they accurately represent the true phenomenon under study."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6gWl5Nf4-Ma7"
   },
   "source": [
    "For example, in the figure below we show a raw set of data from a modern reenactment of Galileo's famous experiment where - in order to quantify the effect of gravity - he repeatedly rolled a ball down a ramp to determine the relationship between distance and amount of time it takes an object to fall to the ground.  Below we show a plot of this dataset, which consists of multiple trials of the same experiment, where each output's numerical value has been rounded to two decimal places.  Note that this is the raw version of the data [shown in example 4 of this set of notes](https://jermwatt.github.io/mlrefined/blog_posts/12_Nonlinear_intro/12_1_Introduction_nonlinear_regression.html).  There are multiple datapoints here that are *duplicates*, which we denote visually by scaling the dot representing each point in the image - the larger the dot, the more duplicate points it represents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369
    },
    "id": "fzfxYfMx-Ma8",
    "outputId": "91d7c15b-6d2e-4ebd-ce3f-9110d7368b21"
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show demo\n",
    "demo1 = section_5_5_helpers.weighted_regression_static_visualizer()\n",
    "demo1.plot_it(data_path_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IBuuv-2R-Ma9"
   },
   "source": [
    "If we examine any regression cost function over such a dataset (i.e., one with repeated entries) we can see that it naturally *collapses* into a weighted version itself.  For example, let us examine the Least Squares cost and suppose that our first two datapoints  $\\left(x_1,\\,y_1\\right)$ and $\\left(x_2,\\,y_2\\right)$ are *identical*.  In this instance - using our `model` notation - the first two summands of our cost function (in the first two datapoints) can be combined since they will always take on the same value\n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{array}\n",
    "\\\n",
    "\\frac{1}{P}\\sum_{p=1}^P \\left(\\text{model}\\left(x_p,\\mathbf{w}\\right) - y_p\\right)^2   \\\\\n",
    "= \\frac{1}{P}\\,2\\,\\left(\\text{model}\\left(x_1,\\mathbf{w}\\right) - y_1\\right)^2 + \\frac{1}{P}\\,0\\,\\left(\\text{model}\\left(x_2,\\mathbf{w}\\right) - y_2\\right)^2  + \\frac{1}{P}\\,\\sum_{p=3}^P \\left(\\text{model}\\left(x_p,\\mathbf{w}\\right) - y_p\\right)^2   \\\\\n",
    "= \\frac{1}{P}\\,2\\,\\left(\\text{model}\\left(x_1,\\mathbf{w}\\right) - y_1\\right)^2 + \\frac{1}{P}\\,\\sum_{p=3}^P \\left(\\text{model}\\left(x_p,\\mathbf{w}\\right) - y_p\\right)^2   \\\\\n",
    "\\end{array}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RR3lbDqq-Ma9"
   },
   "source": [
    "Here we can see that the cost function naturally collapses - in the sense that we can combine summands containing repeated points - so that a repeated point in a dataset is represented *in the cost function* by a single weighted summand.  \n",
    "\n",
    "Of course this holds more generally as well.  If we examined a regression cost function of a dataset having any number of identical points then we can collapse the summands of this cost for each set of identical points just as we have seen here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gW2pBfj5-Ma9"
   },
   "source": [
    "In general this leads to the notion that each term in a regression cost can be *weighted* to reflect repeated points.  We can write such a *weighted regression* Least Squares as\n",
    "\n",
    "\\begin{equation}\n",
    "g\\left(\\mathbf{w}\\right) = \\frac{1}{P}\\sum_{p=1}^P \\beta_p \\left(\\text{model}\\left(x_p,\\mathbf{w}\\right) - y_p\\right)^2\n",
    "\\end{equation}\n",
    "\n",
    "where $\\beta_1,\\,\\beta_2,\\,...,\\,\\beta_P$ are *point-wise* weights.  That is, a unique point $\\left(x_p,\\,y_p\\right)$ in the dataset has weight $\\beta_p = 1$ whereas if this point is repeated $R$ times in the dataset then one instance of it will have weight $\\beta_p = R$ while the others have weight $\\beta_p = 0$.\n",
    "\n",
    "Since these weights are fixed (i.e., they are *not* parameters that need to be tuned, like $\\mathbf{w}$) we can minimize a weighted regression cost precisely as we would any other e.g,. via a local optimization scheme like gradient descent or Newton's method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "l7HQmUaS-Ma-"
   },
   "outputs": [],
   "source": [
    "# a Python implementation of the weighted least squares cost function\n",
    "# setup to compute over mini-batches if desired\n",
    "def least_squares(w,x,y,beta,iter):\n",
    "    # get batch of points\n",
    "    x_p = x[:,iter]\n",
    "    y_p = y[:,iter]\n",
    "    beta_p = beta[:,iter]\n",
    "\n",
    "    # compute cost over batch\n",
    "    cost = np.sum((beta*model(x_p,w) - y_p)**2)\n",
    "    return cost/float(np.size(y_p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u5hz3W5H-Ma-"
   },
   "source": [
    "##  Weighting points by confidence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ouk8zdvH-Ma-"
   },
   "source": [
    "Weighted regression can also be employed when - given knowledge of the process generating a dataset - we want to weight each point based on our *confidence on the trustworthiness* of each datapoint.  For example if our dataset came in two batches - one batch from a trustworthy source and another from a less trustworthy source (where some datapoints could be noisy or fallacious) - we would want to weight datapoints from the trustworthy source more in our final regression.  We can do this very easily using precisely the weighted regression paradigm introduced above, only now we *set the weights $\\beta_1,\\,\\beta_2,\\,...,\\,\\beta_P$ ourselves based on our confidence of each point*.  If we believe that a point is very trustworthy we can set its corresponding weight $\\beta_p$ closer to $1$, and the more untrustworthy we find a point the smaller we set $\\beta_p$ in the range $0 \\leq \\beta_p \\leq 1$ where $\\beta_p = 0$ implies we do not trust the point at all.  In making these weight selections we of course determine *how important each datapoint is* in the training of the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4cq0o6oI-Ma_"
   },
   "source": [
    "Below we show the result of increasing the confidence / weight $\\beta_p$ on a *single point* in a toy dataset, and how this effects a fully trained regression model on a toy linear regression dataset.  This single point is colored red and we show its diameter increasing and as we increase its corresponding weight $\\beta_p$.  With each weighting a weighted Least Squares cost is completely minimized over the entire dataset, and resulting line fit to data (and shown in black).  The higher the weighting of this single point, the more we incentivize a linear regressor to fit to it.   If we increase its weight enough the fully trained regression model naturally starts fitting to this single datapoint alone (disregarding all other points)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 441
    },
    "id": "UDyH8KM4-Ma_",
    "outputId": "c3523ead-edf8-4bc2-ed31-167de351a1f2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show demo\n",
    "demo2 = section_5_5_helpers.weighted_regression_animation_visualizer()\n",
    "demo2.animate_weighting(video_path_1,data_path_2,num_slides = 200,special_ind=9,fps=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "id": "mIXsQkVG-6Wc",
    "outputId": "ed91d437-0241-47fc-b1c8-707e47931c14"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video width=400 controls><source 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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_video(video_path_1, width=400)"
   ]
  }
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